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In this project, we will use machine learning methods to diagnose the health status of bee colonies and individual bees. Bee populations are threatened worldwide due to a number of factors
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Indigenous applicants are strongly encouraged to apply. Ideally the applicants will have an excellent academic track record and/or experience in IT or health IT. Knowledge and skills relating to any
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health “big data”, clinical registries, emergency medicine and orthopaedic trauma. We will leverage from VOTOR, the largest and most comprehensive orthopaedic trauma outcomes registry worldwide to ensure
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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intelligence (AI), can assist in improving health and health care. Radiology images, e.g. X-rays, fundus image, dermascopic image, MRI, CT-scans and EHR, form the basic screening and diagnosis procedure for many
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will support and enhance a wide range of applications such as law enforcement, health, national security, marketing, and advertisement. Required knowledge Essential First class Honors or Masters degree
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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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Synthetic data generation has drawn growing attention due to the lack of training data in many application domains. It is useful for privacy-concerned applications, e.g. digital health applications
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make automated behavioural prediction a routine and simple process for all behavioural researchers. This would enhance the research into almost all mental health disorders. Greater understanding
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reasoning, where better and more transparent reasoning and decision making improve outcomes for end users, providing significant potential health, social and economic benefits. The PhD project will involve